Automatic recognition of suprasegmentals in speech

Abstract

This study reports our efforts to improve automatic recognition of suprasegmentals by fine-tuning wav2vec 2.0 with CTC, a method that has been successful in automatic speech recognition. We demonstrate that the method can improve the state-of-the-art on automatic recognition of syllables, tones, and pitch accents. Utilizing segmental information, by employing tonal finals or tonal syllables as recognition units, can significantly improve Mandarin tone recognition. Language models are helpful when tonal syllables are used as recognition units, but not helpful when tones are recognition units. Finally, Mandarin tone recognition can benefit from English phoneme recognition by combining the two tasks in fine-tuning wav2vec 2.0.

arXiv commentsubmitted to ASRU 2021

References in corpus (4)

• AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines
• Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages
• End-to-End Mandarin Tone Classification with Short Term Context Information
• An Optimized Signal Processing Pipeline for Syllable Detection and Speech Rate Estimation

Cited by in corpus (1)

• Decoupling recognition and transcription in Mandarin ASR